大数据背景下,如何进行项目投资关键影响因素的识别,并据此展开投资预测与分析,是电网投资管理的重要问题。综合运用主成分分析和多元回归模型,系统研究电力工程的投资影响因素,据此构建出基于BP神经网络模型并且经过粒子群算法优化的电力项目投资预测与分析模型,并进行相关实例应用。 Under the background of big data, how to identify the key influencing factors of project investment and start the investment forecast and analysis accordingly is an important issue of power grid investment management. The principal component analysis and multiple regression models are used synthetically to study the influencing factors of investment in power engineering. Based on this, a BP neural network model is constructed and the forecasting and analyzing model of electric power project optimized by particle swarm optimization is constructed. The related examples are also applied.